Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling

Beaulieu, Jake J.; Waldo, Sarah; Balz, David A.; Barnett, Will; Hall, A. D.; Platz, Michelle C.; White, Karen M.

doi:10.1029/2019jg005474

Cited by 39 publications

(36 citation statements)

References 93 publications

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“…It falls in the fourth quintile ( > 60 %) of the reservoir emission rates that included ebullition reported in Deemer et al (2016) ; the warm season

falls in the upper quintile ( > 80 %) of those reservoirs. The warm season

also falls into the upper quartile ( > 75 %) of the 32 temperate reservoirs surveyed by Beaulieu et al (2020) . This result strengthens the finding that midlatitude, eutrophic reservoirs in the midwestern USA can support high CH 4 emission rates (cf.…”

Section: Discussionmentioning

confidence: 99%

Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst

Waldo

Beaulieu

Barnett³

et al. 2021

Biogeosciences

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Abstract. Waters impounded behind dams (i.e., reservoirs) are important sources of greenhouses gases (GHGs), especially methane (CH4), but emission estimates are not well constrained due to high spatial and temporal variability, limitations in monitoring methods to characterize hot spot and hot moment emissions, and the limited number of studies that investigate diurnal, seasonal, and interannual patterns in emissions. In this study, we investigate the temporal patterns and biophysical drivers of CH4 emissions from Acton Lake, a small eutrophic reservoir, using a combination of methods: eddy covariance monitoring, continuous warm-season ebullition measurements, spatial emission surveys, and measurements of key drivers of CH4 production and emission. We used an artificial neural network to gap fill the eddy covariance time series and to explore the relative importance of biophysical drivers on the interannual timescale. We combined spatial and temporal monitoring information to estimate annual whole-reservoir emissions. Acton Lake had cumulative areal emission rates of 45.6 ± 8.3 and 51.4 ± 4.3 g CH4 m−2 in 2017 and 2018, respectively, or 109 ± 14 and 123 ± 10 Mg CH4 in 2017 and 2018 across the whole 2.4 km2 area of the lake. The main difference between years was a period of elevated emissions lasting less than 2 weeks in the spring of 2018, which contributed 17 % of the annual emissions in the shallow region of the reservoir. The spring burst coincided with a phytoplankton bloom, which was likely driven by favorable precipitation and temperature conditions in 2018 compared to 2017. Combining spatially extensive measurements with temporally continuous monitoring enabled us to quantify aspects of the spatial and temporal variability in CH4 emission. We found that the relationships between CH4 emissions and sediment temperature depended on location within the reservoir, and we observed a clear spatiotemporal offset in maximum CH4 emissions as a function of reservoir depth. These findings suggest a strong spatial pattern in CH4 biogeochemistry within this relatively small (2.4 km2) reservoir. In addressing the need for a better understanding of GHG emissions from reservoirs, there is a trade-off in intensive measurements of one water body vs. short-term and/or spatially limited measurements in many water bodies. The insights from multi-year, continuous, spatially extensive studies like this one can be used to inform both the study design and emission upscaling from spatially or temporally limited results, specifically the importance of trophic status and intra-reservoir variability in assumptions about upscaling CH4 emissions.

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“…It falls in the fourth quintile ( > 60 %) of the reservoir emission rates that included ebullition reported in Deemer et al (2016) ; the warm season

falls in the upper quintile ( > 80 %) of those reservoirs. The warm season

Section: Discussionmentioning

confidence: 99%

Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst

Waldo

Beaulieu

Barnett³

et al. 2021

Biogeosciences

Self Cite

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“…Analyses were performed using boosted regression tree analysis (BRT) (Elith et al, 2008;Beaulieu et al, 2020;Jarvis et al, 2020), which is a machine learning technique that tests for relationships between predictors (in this case, physical and chemical variables, see Table 1) and response variables (here three macrophyte indicators in shallow lakes and Chla) in a dataset. The principle is that a second model is ''boosted'' from the previous one by minimising its errors in order to achieve the best new model derived through a model simplification process, where each predictor is left out sequentially and the reduction in the predictive power is used to determine if it should stay in or not.…”

Section: Data Analysesmentioning

confidence: 99%

Submerged macrophytes in Danish lakes: impact of morphological and chemical factors on abundance and species richness

et al. 2021

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We analysed long-term monitoring data on submerged macrophytes and water chemistry from 666 Danish lakes[ 1 hectare and mean depth\ 3 m, encompassing a total of 1447 lake years. Our aim was to describe how plant cover (COV), plant volume inhabited (PVI) and species richness related to physical and chemical and environmental variables. Boosted regression tree (BRT) analyses revealed that chlorophyll a, Secchi depth and depth were the strongest predictors of COV and PVI. Chlorophyll had a strong negative effect up to 50 lg/l, whereas the changes related to Secchi depth and depth were more gradual and covered more of the gradient. Macrophyte species richness was best predicted by lake area and alkalinity, with chlorophyll a, nutrients and colour having significant but less marked effects. For chlorophyll a, 78% of the observed variance could be explained by the BRT model, with the most powerful predictors being both phosphorus and nitrogen, but with significant additional effects of plant cover and alkalinity. Our analyses revealed limited direct effect of nutrients on macrophyte abundance, but an indirect hierarchical effect of nutrients mediated through chlorophyll a with additional interactive effects by plant cover itself, alkalinity, mean depth and colour.

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“…Most attempts are centered on upscaling the GHG emission rates from individual waterbodies to the regional or global estimates and simply multiplying an average emission rate by the total waterbody surface area in the region of interest [8][9][10][11]. It is pointed out in [12] that this upscaling approach can be highly biased unless the emission rate measurements come from a representative sample of lakes or reservoirs in the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Methane Emissions from Reservoirs Based on Country-Specific Trophic State Assessment in Korea

Chung

Paik

Kim

2022

Water

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It has been reported that significant quantities of greenhouse gases are emitted from wetlands, from which emissions and their contributions to global warming have received much less attentions. Thus, a refinement to the previous published guidelines has recently been made to provide an updated and sound scientific basis for the purpose of supporting the preparation of national inventories. This study is aimed at demonstrating the applicability of the refinement for estimating methane emissions from reservoirs in the Republic of Korea. It is desirable to take the direct measurement of total methane fluxes across the reservoir surface, which may require a substantial amount of research efforts though. Alternatively, methane emissions from individual reservoirs may be estimated with relevant parameters accounting for the regional environmental characteristics. The assessment of trophic state has been employed to better represent the emissions behavior of reservoirs, based on which the methane emissions from local reservoirs in Korea are estimated. It is noted that the country has developed its own water quality index with the consideration of environmental characteristics. The seasonal variations in methane emissions are tested for their statistical significance and it is proposed that the emission estimates can be predicted from the trophic state assessment with the application of regression analysis. Following the guidelines prescribed by the refinement and procedures outlined in this study, the results from emissions estimation and prediction can be effectively used for the improvement of national inventories.

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Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling

Cited by 39 publications

References 93 publications

Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst

Temporal trends in methane emissions from a small eutrophic reservoir: the key role of a spring burst

Submerged macrophytes in Danish lakes: impact of morphological and chemical factors on abundance and species richness

Estimation of Methane Emissions from Reservoirs Based on Country-Specific Trophic State Assessment in Korea

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